NeuroVista Inc

Seattle, WA, United States

NeuroVista Inc

Seattle, WA, United States
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Neurovista Holdings Inc., Cyberonics, Neurovista Corporation and BioNeuronics Corporation | Date: 2010-08-10

computer hardware; computer software for use with neurological monitors and/or neurological stimulators that are used to monitor and manage neurological and physiological disorders. implantable and noninvasive medical devices, namely, neurological monitors, monitoring leads and medical telemetry apparatus used with neurological monitors to diagnose, monitor and/or manage neurological and physiological disorders.


Cook M.J.,University of Melbourne | Varsavsky A.,University of Melbourne | Himes D.,Neurovista Corporation | Leyde K.,Neurovista Corporation | And 3 more authors.
Frontiers in Neurology | Year: 2014

The pattern of epileptic seizures is often considered unpredictable, and the interval between events without correlation. A number of studies have examined the possibility that seizure activity respects a power-law relationship, both in terms of event magnitude and inter-event intervals. Such relationships are found in a variety of natural and manmade systems, such as earthquakes or Internet traffic, and describe the relationship between the magnitude of an event and the number of events. We postulated that human inter-seizure intervals would follow a power law relationship, and furthermore that evidence for the existence of a long memory process could be established in this relationship. We performed a post-hoc analysis, studying 8 patients who had long-term (up to 2 years) ambulatory intracranial EEG data recorded as part of the assessment of a novel seizure prediction device. We demonstrated that a power law relationship could be established in these patients (β =-1.5). In 5 out of the 6 subjects whose data was sufficiently stationary for analysis, we found evidence of long memory between epileptic events. This memory spans time scales from 30 minutes to 40 days. The estimated Hurst exponents range from 0.51-0.77±0.01. This finding may provide evidence of phasetransitions underlying the dynamics of epilepsy. © 2014 Cook, Varsavsky, Himes, Leyde, Berkovic, O'brien and Mareels.


Patent
NeuroVista Corporation | Date: 2010-05-05

The present invention provides methods and systems for modulating a patients neurological disease state. In one embodiment, the system comprises one or more sensors that sense at least one signal that comprise a characteristic that is indicative of a neurological disease state. A signal processing assembly is in communication with the one or more sensors and processes the at least one signal to estimate the neurological disease state and to generate a therapy to the patient that is based at least in part on the estimated neurological disease state. A treatment assembly is in communication with the signal processing assembly and delivers the therapy to a nervous system component of the patient.


Patent
Neurovista Corporation | Date: 2012-10-23

Systems and methods for neuromonitoring a subject are described. The system may include a stimulation assembly including a pulse generator that generates one or more stimulus waveforms; an electrode array coupled to the stimulation assembly and configured to deliver a stimulation signal to nervous system of the subject; a sensing assembly adapted to acquire a signal from a subject indicative of the subjects brain activity; a power supply configured to supply power to the stimulation assembly and the sensing assembly; and a timing controller programmed to control the use of the power supply by the stimulation assembly and the sensing assembly, said timing controller being programmed to control the time the sensing assembly is powered to acquire the signal to be substantially different than the time the stimulation assembly is powered to stimulate the subject.


Systems and methods for enhancing the accuracy of classifying a measurement by providing an artificial class. Seizure prediction systems may employ a classification system including an artificial class and a user interface for signaling uncertainty in classification when a measurement is classified in the artificial class.


Systems and methods for enhancing the accuracy of classifying a measurement by providing an artificial class. Seizure prediction systems may employ a classification system including an artificial class and a user interface for signaling uncertainty in classification when a measurement is classified in the artificial class.


Systems and methods for enhancing the accuracy of classifying a measurement by providing an artificial class. Seizure prediction systems may employ a classification system including an artificial class and a user interface for signaling uncertainty in classification when a measurement is classified in the artificial class.


Patent
Neurovista Corporation | Date: 2012-02-13

Methods of classifying a subjects condition are described. The method includes: receiving measured signals from the subject; processing the measured signals using a computing device to identify a class associated with an identified condition of the subject; introducing an artificial class, the artificial class being associated with an unknown condition of the subject; classifying a feature vector from the subject into the identified class or the artificial class; and generating a signal in response to classifying the feature vector. The measured signals from the subject may include at least one signal extracted from brain activity of the subject.


PubMed | NeuroVista Corporation, Royal Melbourne Hospital and University of Melbourne
Type: Journal Article | Journal: Brain : a journal of neurology | Year: 2016

We report on a quantitative analysis of electrocorticography data from a study that acquired continuous ambulatory recordings in humans over extended periods of time. The objectives were to examine patterns of seizures and spontaneous interictal spikes, their relationship to each other, and the nature of periodic variation. The recorded data were originally acquired for the purpose of seizure prediction, and were subsequently analysed in further detail. A detection algorithm identified potential seizure activity and a template matched filter was used to locate spikes. Seizure events were confirmed manually and classified as either clinically correlated, electroencephalographically identical but not clinically correlated, or subclinical. We found that spike rate was significantly altered prior to seizure in 9 out of 15 subjects. Increased pre-ictal spike rate was linked to improved predictability; however, spike rate was also shown to decrease before seizure (in 6 out of the 9 subjects). The probability distribution of spikes and seizures were notably similar, i.e. at times of high seizure likelihood the probability of epileptic spiking also increased. Both spikes and seizures showed clear evidence of circadian regulation and, for some subjects, there were also longer term patterns visible over weeks to months. Patterns of spike and seizure occurrence were highly subject-specific. The pre-ictal decrease in spike rate is not consistent with spikes promoting seizures. However, the fact that spikes and seizures demonstrate similar probability distributions suggests they are not wholly independent processes. It is possible spikes actively inhibit seizures, or that a decreased spike rate is a secondary symptom of the brain approaching seizure. If spike rate is modulated by common regulatory factors as seizures then spikes may be useful biomarkers of cortical excitability.


PubMed | NeuroVista Corporation, Royal Melbourne Hospital, Austin and Repatriation Medical Center and University of Melbourne
Type: Journal Article | Journal: Epilepsia | Year: 2016

We report on a quantitative analysis of data from a study that acquired continuous long-term ambulatory human electroencephalography (EEG) data over extended periods. The objectives were to examine the seizure duration and interseizure interval (ISI), their relationship to each other, and the effect of these features on the clinical manifestation of events.Chronic ambulatory intracranial EEG data acquired for the purpose of seizure prediction were analyzed and annotated. A detection algorithm identified potential seizure activity, which was manually confirmed. Events were classified as clinically corroborated, electroencephalographically identical but not clinically corroborated, or subclinical. K-means cluster analysis supplemented by finite mixture modeling was used to locate groupings of seizure duration and ISI.Quantitative analyses confirmed well-resolved groups of seizure duration and ISIs, which were either mono-modal or multimodal, and highly subject specific. Subjects with a single population of seizures were linked to improved seizure prediction outcomes. There was a complex relationship between clinically manifest seizures, seizure duration, and interval.These data represent the first opportunity to reliably investigate the statistics of seizure occurrence in a realistic, long-term setting. The presence of distinct duration groups implies that the evolution of seizures follows a predetermined course. Patterns of seizure activity showed considerable variation between individuals, but were highly predictable within individuals. This finding indicates seizure dynamics are characterized by subject-specific time scales; therefore, temporal distributions of seizures should also be interpreted on an individual level. Identification of duration and interval subgroups may provide a new avenue for improving seizure prediction.

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